Figure REF  demonstrates the performance of different
distance measurements in terms of varying sequence length. From Figure REF , we can make three main observations:
(1) It shows that Euclidean distance scales poorly to long sequences. When the sequence length
exceeds 400, Euclidean distance begins to degrade the generalization
performance of 1NN classifier. This result is as expected and consistent
with our intuition that Euclidean distance is not suitable for
long series.
(2) The performance of DTW initially improves when facing
long sequences. Its performance is much better than Euclidean distance. This
result explains why DTW is so successful in sequence processing domains [1]}, [2]}. Sequences are usually long
and the time points are usually much larger than the number of observations. However, when
sequence length grows longer than 600 points, the performance of DTW begins to
worsen. (3) Unlike the competitive distance measurements, DSW is
consistently improving the performance in our experiment.
